Hospitals using predictive modeling for population health
A new study found that 15 percent of providers are using predictive modeling within hospitals.
The most common uses for predictive analytics were readmissions, patient deterioration, sepsis and general population health. Jvion, a healthcare technology and software company, conducted the study this month.
Of the providers using predictive modeling, 92 percent used the information to predict patient risk or illness.
Jvion defined advanced predictive modeling as “the application of machine learning algorithms to find patterns within data to predict patient-level risk.”
“The survey findings point to a growing need within the provider community for solutions that help prevent patient illness through real-time predictions,” Jvion Vice President Todd Schlesinger said in a news release. “With so much changing in the industry, providers are hungry for analytics that will help them improve health outcomes while reducing risk and waste across the system.”
Executive Editor
Tim Casey joined TriMed Media Group in 2015 as Executive Editor. For the previous four years, he worked as an editor and writer for HMP Communications, primarily focused on covering managed care issues and reporting from medical and health care conferences. He was also a staff reporter at the Sacramento Bee for more than four years covering professional, college and high school sports. He earned his undergraduate degree in psychology from the University of Notre Dame and his MBA degree from Georgetown University.